An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network
This addresses the time-consuming task of grading SQL queries for educators, but appears incremental as it builds on existing automated grading systems.
The paper tackles the problem of automating SQL query grading by introducing a novel convolutional neural network architecture with parameter-sharing, achieving improved understanding of SQL statements.
Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an attempt to mitigate these challenges, but those systems have their own limitations. This paper describes our novel approach to automating the process of grading SQL queries. Unlike previous approaches, we employ a unique convolutional neural network architecture that employs a parameter-sharing approach for different machine learning tasks that enables the architecture to induce different knowledge representations of the data to increase its potential for understanding SQL statements.